Semantic segmentation of land cover classes is fundamental for agricultural
and economic development work, from sustainable forestry to urban planning, yet
existing training datasets have significant limitations. To generate an open
and comprehensive training library of high resolution Earth imagery and high
quality land cover classifications, public Sentinel-2 data at 10 m spatial
resolution was matched with accurate GlobeLand30 labels from 2010, which were
filtered by agreement with an intermediary Sentinel-2 classification at 20 m
produced during atmospheric correction. Scene-level classifications were
predicted by Random Forests trained on valid reflectance data and the filtered
labels, and achieved over 80% model accuracy for a variety of locations.
Further work is required to aggregate individual scene classifications for
annual labels and to test the approach in more locations, before crowdsourcing
human validation. The goal is to create a sustained community-wide effort to
generate image labels not only for land cover, but also very specific images
for major agriculture crops across the world and other thematic categories of
interest to the global development community.

使用 Sentinel-2 卫星数据和 Random Forests 算法的语义分割技术可用于高精度地图绘制，为农业和经济发展提供一种可行技术。

生成土地覆盖分类训练数据集以推进全球发展

Generating a Training Dataset for Land Cover Classification to Advance  Global Development

Semantic labeling (or pixel-level land-cover classification) in ultra-high
resolution imagery (< 10cm) requires statistical models able to learn high
level concepts from spatial data, with large appearance variations.
Convolutional Neural Networks (CNNs) achieve this goal by learning
discriminatively a hierarchy of representations of increasing abstraction.
In this paper we present a CNN-based system relying on an
downsample-then-upsample architecture. Specifically, it first learns a rough
spatial map of high-level representations by means of convolutions and then
learns to upsample them back to the original resolution by deconvolutions. By
doing so, the CNN learns to densely label every pixel at the original
resolution of the image. This results in many advantages, including i)
state-of-the-art numerical accuracy, ii) improved geometric accuracy of
predictions and iii) high efficiency at inference time.
We test the proposed system on the Vaihingen and Potsdam sub-decimeter
resolution datasets, involving semantic labeling of aerial images of 9cm and
5cm resolution, respectively. These datasets are composed by many large and
fully annotated tiles allowing an unbiased evaluation of models making use of
spatial information. We do so by comparing two standard CNN architectures to
the proposed one: standard patch classification, prediction of local label
patches by employing only convolutions and full patch labeling by employing
deconvolutions. All the systems compare favorably or outperform a
state-of-the-art baseline relying on superpixels and powerful appearance
descriptors. The proposed full patch labeling CNN outperforms these models by a
large margin, also showing a very appealing inference time.

本文基于卷积神经网络提出了一种深度学习系统，能够在高分辨率的航拍图像上检测地物信息并达到了良好的识别精度。